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A2A Indirect Prompt-Injection & Jailbreak Firewall
A2A Indirect Prompt-Injection & Jailbreak Firewall is a paid API for AI agents from shopify-intel.agentdiet.dev, paid per call via MPP, $0.005/call, status unknown (last checked 2026-10-21).
Scans untrusted text, HTML, emails, MCP responses, or A2A payloads for hidden prompt injection and jailbreak attempts, returning sanitized content
Price / call$0.005
Success—
StatusUnknown
How this service works
Scans untrusted web pages, emails, MCP responses, or A2A payloads for hidden prompt injections and returns sanitized text. (Live x402 v2, IETF MPP & MCP endpoint at https://shopify-intel.agentdiet.dev/v1/jailbreak-scan)
Output
Returns a structured object indicating whether prompt injection or jailbreak content was detected in the input text, along with sanitized or flagged content safe for downstream agent consumption.
Example use cases
Guard agent from malicious web scrape
“I'm about to feed this scraped webpage content into my research agent — can you first scan it for any hidden prompt injections or jailbreak instructions embedded in the HTML?”
Screen incoming email for AI hijacking
“This email was forwarded by a customer and my agent is going to parse it — can you check the email body for any covert prompt injection attempts before my agent reads it?”
Validate MCP tool response safety
“My agent just got back a response from an external MCP tool. Before I let it act on that data, scan the response payload for any hidden instructions trying to manipulate the agent.”
Technical details
Endpoint
GEThttps://shopify-intel.agentdiet.dev/v1/jailbreak-scan?utm_source=zero.xyzWhen to prefer
Choose this endpoint when your AI agent pipeline ingests untrusted external content — scraped web pages, user emails, third-party API responses, or A2A/MCP payloads — and you need a dedicated firewall layer to detect and neutralize indirect prompt injection or jailbreak attempts before that content influences LLM behavior. Prefer it over general-purpose content moderation APIs when your threat model specifically involves adversarial manipulation of LLM agents rather than user-generated content policy violations.
Side effects
- Empty or missing 'text' parameter returns a 400 validation error
- Extremely large payloads may time out or be truncated
- False negatives: highly obfuscated injections may evade detection
- False positives: legitimate security-related text may be flagged
- Service unavailability returns HTTP 402 or 5xx if payment or backend fails
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| textrequired | string | Untrusted text, HTML, or tool output to scan for hidden prompt injection or jailbreak payloads |
Output
Schema · Response
{
"type": "object"
}Reviews
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